{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Run the fusion EasyVVUQ campaign\n", "\n", "Run an EasyVVUQ campaign to analyze the sensitivity of the temperature\n", "profile predicted by a simplified model of heat conduction in a\n", "tokamak plasma.\n", "\n", "This is done with SC." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:30:34.466047Z", "start_time": "2021-06-07T14:30:30.239611Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "# import packages that we will use\n", "\n", "import os\n", "import easyvvuq as uq\n", "import chaospy as cp\n", "import pickle\n", "import time\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib\n", "if not os.getenv(\"DISPLAY\"): matplotlib.use('Agg')\n", "import matplotlib.pylab as plt\n", "from IPython.display import display\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:30:34.620890Z", "start_time": "2021-06-07T14:30:34.467038Z" }, "code_folding": [ 0, 2, 4 ] }, "outputs": [], "source": [ "# we need fipy -- install if not already available\n", "\n", "try:\n", " import fipy\n", "except ModuleNotFoundError:\n", " ! pip install future\n", " ! pip install fipy\n", " import fipy" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:30:34.625443Z", "start_time": "2021-06-07T14:30:34.622815Z" }, "code_folding": [ 0, 2 ] }, "outputs": [], "source": [ "# routine to write out (if needed) the fusion .template file\n", "\n", "def write_template(params):\n", " str = \"\"\n", " first = True\n", " for k in params.keys():\n", " if first:\n", " str += '{\"%s\": \"$%s\"' % (k,k) ; first = False\n", " else:\n", " str += ', \"%s\": \"$%s\"' % (k,k)\n", " str += '}'\n", " print(str, file=open('fusion.template','w'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:30:34.630975Z", "start_time": "2021-06-07T14:30:34.626852Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "# define parameters of the fusion model\n", "def define_params():\n", " return {\n", " \"Qe_tot\": {\"type\": \"float\", \"min\": 1.0e6, \"max\": 50.0e6, \"default\": 2e6},\n", " \"H0\": {\"type\": \"float\", \"min\": 0.00, \"max\": 1.0, \"default\": 0},\n", " \"Hw\": {\"type\": \"float\", \"min\": 0.01, \"max\": 100.0, \"default\": 0.1},\n", " \"Te_bc\": {\"type\": \"float\", \"min\": 10.0, \"max\": 1000.0, \"default\": 100},\n", " \"chi\": {\"type\": \"float\", \"min\": 0.01, \"max\": 100.0, \"default\": 1},\n", " \"a0\": {\"type\": \"float\", \"min\": 0.2, \"max\": 10.0, \"default\": 1},\n", " \"R0\": {\"type\": \"float\", \"min\": 0.5, \"max\": 20.0, \"default\": 3},\n", " \"E0\": {\"type\": \"float\", \"min\": 1.0, \"max\": 10.0, \"default\": 1.5},\n", " \"b_pos\": {\"type\": \"float\", \"min\": 0.95, \"max\": 0.99, \"default\": 0.98},\n", " \"b_height\": {\"type\": \"float\", \"min\": 3e19, \"max\": 10e19, \"default\": 6e19},\n", " \"b_sol\": {\"type\": \"float\", \"min\": 2e18, \"max\": 3e19, \"default\": 2e19},\n", " \"b_width\": {\"type\": \"float\", \"min\": 0.005, \"max\": 0.025, \"default\": 0.01},\n", " \"b_slope\": {\"type\": \"float\", \"min\": 0.0, \"max\": 0.05, \"default\": 0.01},\n", " \"nr\": {\"type\": \"integer\", \"min\": 10, \"max\": 1000, \"default\": 100},\n", " \"dt\": {\"type\": \"float\", \"min\": 1e-3, \"max\": 1e3, \"default\": 100},\n", " \"out_file\": {\"type\": \"string\", \"default\": \"output.csv\"}\n", " }" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:30:34.637058Z", "start_time": "2021-06-07T14:30:34.632103Z" }, "code_folding": [ 0, 2, 17, 21, 28 ] }, "outputs": [], "source": [ "# define varying quantities\n", "def define_vary():\n", " vary_all = {\n", " \"Qe_tot\": cp.Uniform(1.8e6, 2.2e6),\n", " \"H0\": cp.Uniform(0.0, 0.2),\n", " \"Hw\": cp.Uniform(0.1, 0.5),\n", " \"chi\": cp.Uniform(0.8, 1.2),\n", " \"Te_bc\": cp.Uniform(80.0, 120.0),\n", " \"a0\": cp.Uniform(0.9, 1.1),\n", " \"R0\": cp.Uniform(2.7, 3.3),\n", " \"E0\": cp.Uniform(1.4, 1.6),\n", " \"b_pos\": cp.Uniform(0.95, 0.99),\n", " \"b_height\": cp.Uniform(5e19, 7e19),\n", " \"b_sol\": cp.Uniform(1e19, 3e19),\n", " \"b_width\": cp.Uniform(0.015, 0.025),\n", " \"b_slope\": cp.Uniform(0.005, 0.020)\n", " }\n", " vary_2 = {\n", " \"Qe_tot\": cp.Uniform(1.8e6, 2.2e6),\n", " \"Te_bc\": cp.Uniform(80.0, 120.0)\n", " }\n", " vary_5 = {\n", " \"Qe_tot\": cp.Uniform(1.8e6, 2.2e6),\n", " \"H0\": cp.Uniform(0.0, 0.2),\n", " \"Hw\": cp.Uniform(0.1, 0.5),\n", " \"chi\": cp.Uniform(0.8, 1.2),\n", " \"Te_bc\": cp.Uniform(80.0, 120.0)\n", " }\n", " vary_10 = {\n", " \"Qe_tot\": cp.Uniform(1.8e6, 2.2e6),\n", " \"H0\": cp.Uniform(0.0, 0.2),\n", " \"Hw\": cp.Uniform(0.1, 0.5),\n", " \"chi\": cp.Uniform(0.8, 1.2),\n", " \"Te_bc\": cp.Uniform(80.0, 120.0),\n", " \"b_pos\": cp.Uniform(0.95, 0.99),\n", " \"b_height\": cp.Uniform(5e19, 7e19),\n", " \"b_sol\": cp.Uniform(1e19, 3e19),\n", " \"b_width\": cp.Uniform(0.015, 0.025),\n", " \"b_slope\": cp.Uniform(0.005, 0.020)\n", " }\n", " return vary_5" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:30:34.640970Z", "start_time": "2021-06-07T14:30:34.638336Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "# define a model to run the fusion code directly from python, expecting a dictionary and returning a dictionary\n", "def run_fusion_model(input):\n", " import json\n", " import fusion\n", " qois = [\"te\", \"ne\", \"rho\", \"rho_norm\"]\n", " del input['out_file']\n", " return {q: v for q,v in zip(qois, [t.tolist() for t in fusion.solve_Te(**input, plots=False, output=False)])}" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:30:34.652600Z", "start_time": "2021-06-07T14:30:34.644506Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "# routine to run a SC campaign\n", "\n", "def run_sc_case(sc_order=2, local=True, dask=True, batch_size=os.cpu_count(), use_files=True):\n", " \"\"\"\n", " Inputs:\n", " sc_order: order of the sc expansion\n", " local: if using Dask, whether to use the local option (True)\n", " dask: whether to use dask (True)\n", " batch_size: for the non Dask option, number of cases to run in parallel (16)\n", " Outputs:\n", " results_df: Pandas dataFrame containing inputs to and output from the model\n", " results: Results of the sc analysis\n", " times: Information about the elapsed time for the various phases of the calculation\n", " sc_order: sc_order \n", " count: number of sc samples\n", " \"\"\"\n", " \n", " if dask:\n", " if local:\n", " print('Running locally')\n", " import multiprocessing.popen_spawn_posix\n", " from dask.distributed import Client, LocalCluster\n", " cluster = LocalCluster(threads_per_worker=1)\n", " client = Client(cluster) # processes=True, threads_per_worker=1)\n", " else:\n", " print('Running using SLURM')\n", " from dask.distributed import Client\n", " from dask_jobqueue import SLURMCluster\n", " cluster = SLURMCluster(\n", " job_extra=['--qos=p.tok.openmp.2h', '--mail-type=end', '--mail-user=dpc@rzg.mpg.de', '-t 2:00:00'], \n", " queue='p.tok.openmp', \n", " cores=8, \n", " memory='8 GB',\n", " processes=8)\n", " cluster.scale(32)\n", " print(cluster)\n", " print(cluster.job_script())\n", " client = Client(cluster)\n", " print(client)\n", "\n", " else:\n", " import concurrent.futures\n", "# client = concurrent.futures.ProcessPoolExecutor(max_workers=batch_size)\n", " client = concurrent.futures.ThreadPoolExecutor(max_workers=batch_size)\n", "# client = None\n", " \n", " times = np.zeros(7)\n", "\n", " time_start = time.time()\n", " time_start_whole = time_start\n", " # Set up a fresh campaign called \"fusion_sc.\"\n", " my_campaign = uq.Campaign(name='fusion_sc.') \n", "\n", " # Define parameter space\n", " params = define_params()\n", "\n", " # Create an encoder and decoder for sc test app\n", " if use_files:\n", " encoder = uq.encoders.GenericEncoder(template_fname='fusion.template',\n", " delimiter='$',\n", " target_filename='fusion_in.json')\n", "\n", "\n", " decoder = uq.decoders.SimpleCSV(target_filename=\"output.csv\",\n", " output_columns=[\"te\", \"ne\", \"rho\", \"rho_norm\"])\n", "\n", " execute = uq.actions.ExecuteLocal('python3 %s/fusion_model.py fusion_in.json' % (os.getcwd()))\n", "\n", " actions = uq.actions.Actions(uq.actions.CreateRunDirectory('/tmp'), \n", " uq.actions.Encode(encoder), execute, uq.actions.Decode(decoder))\n", " else:\n", " actions = uq.actions.Actions(uq.actions.ExecutePython(run_fusion_model))\n", "\n", "\n", " # Add the app (automatically set as current app)\n", " my_campaign.add_app(name=\"fusion\", params=params, actions=actions)\n", "\n", " time_end = time.time()\n", " times[1] = time_end-time_start\n", " print('Time for phase 1 = %.3f' % (times[1]))\n", "\n", " time_start = time.time()\n", " # Associate a sampler with the campaign\n", " my_campaign.set_sampler(uq.sampling.SCSampler(vary=define_vary(), polynomial_order=sc_order))\n", " my_campaign.draw_samples()\n", " print('Number of samples = %s' % my_campaign.get_active_sampler().count)\n", "\n", " time_end = time.time()\n", " times[2] = time_end-time_start\n", " print('Time for phase 2 = %.3f' % (times[2]))\n", "\n", " time_start = time.time()\n", " # Perform the actions\n", " my_campaign.execute(pool=client).collate(progress_bar=True)\n", "\n", " if dask:\n", " client.close()\n", " client.shutdown()\n", "\n", " time_end = time.time()\n", " times[3] = time_end-time_start\n", " print('Time for phase 3 = %.3f' % (times[3]))\n", "\n", " time_start = time.time()\n", " # Collate the results\n", " results_df = my_campaign.get_collation_result()\n", "\n", " time_end = time.time()\n", " times[4] = time_end-time_start\n", " print('Time for phase 4 = %.3f' % (times[4]))\n", "\n", " time_start = time.time()\n", " # Post-processing analysis\n", " results = my_campaign.analyse(qoi_cols=[\"te\", \"ne\", \"rho\", \"rho_norm\"])\n", "\n", " time_end = time.time()\n", " times[5] = time_end-time_start\n", " print('Time for phase 5 = %.3f' % (times[5]))\n", "\n", " time_start = time.time()\n", " # Save the results\n", " pickle.dump(results, open('fusion_results.pickle','bw'))\n", " time_end = time.time()\n", " times[6] = time_end-time_start\n", " print('Time for phase 6 = %.3f' % (times[6]))\n", "\n", " times[0] = time_end - time_start_whole\n", "\n", " return results_df, results, times, sc_order, my_campaign.get_active_sampler().count" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:30:34.668084Z", "start_time": "2021-06-07T14:30:34.653729Z" }, "code_folding": [ 0, 2, 24, 46, 58, 72, 84 ] }, "outputs": [], "source": [ "# routines for plotting the results\n", "\n", "def plot_Te(results, title=None):\n", " # plot the calculated Te: mean, with std deviation, 1, 10, 90 and 99%\n", " plt.figure()\n", " rho = results.describe('rho', 'mean')\n", " plt.plot(rho, results.describe('te', 'mean'), 'b-', label='Mean')\n", " plt.plot(rho, results.describe('te', 'mean')-results.describe('te', 'std'), 'b--', label='+1 std deviation')\n", " plt.plot(rho, results.describe('te', 'mean')+results.describe('te', 'std'), 'b--')\n", " plt.fill_between(rho, results.describe('te', 'mean')-results.describe('te', 'std'), results.describe('te', 'mean')+results.describe('te', 'std'), color='b', alpha=0.2)\n", " try:\n", " plt.plot(rho, results.describe('te', '10%'), 'b:', label='10 and 90 percentiles')\n", " plt.plot(rho, results.describe('te', '90%'), 'b:')\n", " plt.fill_between(rho, results.describe('te', '10%'), results.describe('te', '90%'), color='b', alpha=0.1)\n", " plt.fill_between(rho, results.describe('te', '1%'), results.describe('te', '99%'), color='b', alpha=0.05)\n", " except:\n", " print('Problem with some of the percentiles')\n", " plt.legend(loc=0)\n", " plt.xlabel('rho [$m$]')\n", " plt.ylabel('Te [$eV$]')\n", " if not title is None: plt.title(title)\n", " plt.savefig('Te.png')\n", " plt.savefig('Te.pdf')\n", "\n", "def plot_ne(results, title=None):\n", " # plot the calculated ne: mean, with std deviation, 1, 10, 90 and 99%\n", " plt.figure()\n", " rho = results.describe('rho', 'mean')\n", " plt.plot(rho, results.describe('ne', 'mean'), 'b-', label='Mean')\n", " plt.plot(rho, results.describe('ne', 'mean')-results.describe('ne', 'std'), 'b--', label='+1 std deviation')\n", " plt.plot(rho, results.describe('ne', 'mean')+results.describe('ne', 'std'), 'b--')\n", " plt.fill_between(rho, results.describe('ne', 'mean')-results.describe('ne', 'std'), results.describe('ne', 'mean')+results.describe('ne', 'std'), color='b', alpha=0.2)\n", " try:\n", " plt.plot(rho, results.describe('ne', '10%'), 'b:', label='10 and 90 percentiles')\n", " plt.plot(rho, results.describe('ne', '90%'), 'b:')\n", " plt.fill_between(rho, results.describe('ne', '10%'), results.describe('ne', '90%'), color='b', alpha=0.1)\n", " plt.fill_between(rho, results.describe('ne', '1%'), results.describe('ne', '99%'), color='b', alpha=0.05)\n", " except:\n", " print('Problem with some of the percentiles')\n", " plt.legend(loc=0)\n", " plt.xlabel('rho [$m$]')\n", " plt.ylabel('ne [$m^{-3}$]')\n", " if not title is None: plt.title(title)\n", " plt.savefig('ne.png')\n", " plt.savefig('ne.pdf')\n", "\n", "def plot_sobols_first(results, title=None, field='te'):\n", " # plot the first Sobol results\n", " plt.figure()\n", " rho = results.describe('rho', 'mean')\n", " for k in results.sobols_first()[field].keys(): plt.plot(rho, results.sobols_first()[field][k], label=k)\n", " plt.legend(loc=0)\n", " plt.xlabel('rho [$m$]')\n", " plt.ylabel('sobols_first')\n", " if not title is None: plt.title(field + ': ' + title)\n", " plt.savefig('sobols_first_%s.png' % (field))\n", " plt.savefig('sobols_first_%s.pdf' % (field))\n", "\n", "def plot_sobols_second(results, title=None, field='te'):\n", " # plot the second Sobol results\n", " plt.figure()\n", " rho = results.describe('rho', 'mean')\n", " for k1 in results.sobols_second()[field].keys():\n", " for k2 in results.sobols_second()[field][k1].keys():\n", " plt.plot(rho, results.sobols_second()[field][k1][k2], label=k1+'/'+k2)\n", " plt.legend(loc=0, ncol=2)\n", " plt.xlabel('rho [$m$]')\n", " plt.ylabel('sobols_second')\n", " if not title is None: plt.title(field + ': ' + title)\n", " plt.savefig('sobols_second_%s.png' % (field))\n", " plt.savefig('sobols_second_%s.pdf' % (field))\n", "\n", "def plot_sobols_total(results, title=None, field='te'):\n", " # plot the total Sobol results\n", " plt.figure()\n", " rho = results.describe('rho', 'mean')\n", " for k in results.sobols_total()[field].keys(): plt.plot(rho, results.sobols_total()[field][k], label=k)\n", " plt.legend(loc=0)\n", " plt.xlabel('rho [$m$]')\n", " plt.ylabel('sobols_total')\n", " if not title is None: plt.title(field + ': ' + title)\n", " plt.savefig('sobols_total_%s.png' % (field))\n", " plt.savefig('sobols_total_%s.pdf' % (field))\n", "\n", "def plot_distribution(results, results_df, title=None):\n", " te_dist = results.raw_data['output_distributions']['te']\n", " rho_norm = results.describe('rho_norm', 'mean')\n", " for i in [np.maximum(0, int(i-1)) \n", " for i in np.linspace(0,1,5) * rho_norm.shape]:\n", " plt.figure()\n", " pdf_raw_samples = cp.GaussianKDE(results_df.te[i])\n", " pdf_kde_samples = cp.GaussianKDE(te_dist.samples[i])\n", " plt.hist(results_df.te[i], density=True, bins=50, label='histogram of raw samples', alpha=0.25)\n", " if hasattr(te_dist, 'samples'):\n", " plt.hist(te_dist.samples[i], density=True, bins=50, label='histogram of kde samples', alpha=0.25)\n", "\n", " plt.plot(np.linspace(pdf_raw_samples.lower, pdf_raw_samples.upper), pdf_raw_samples.pdf(np.linspace(pdf_raw_samples.lower, pdf_raw_samples.upper)), label='PDF (raw samples)')\n", " plt.plot(np.linspace(pdf_kde_samples.lower, pdf_kde_samples.upper), pdf_kde_samples.pdf(np.linspace(pdf_kde_samples.lower, pdf_kde_samples.upper)), label='PDF (kde samples)')\n", "\n", " plt.legend(loc=0)\n", " plt.xlabel('Te [$eV$]')\n", " if title is None:\n", " plt.title('Distributions for rho_norm = %0.4f' % (rho_norm[i]))\n", " else:\n", " plt.title('%s\\nDistributions for rho_norm = %0.4f' % (title, rho_norm[i]))\n", " plt.savefig('distribution_function_rho_norm=%0.4f.png' % (rho_norm[i]))\n", " plt.savefig('distribution_function_rho_norm=%0.4f.pdf' % (rho_norm[i]))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:46:31.401457Z", "start_time": "2021-06-07T14:30:34.671063Z" }, "code_folding": [ 0 ], "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/32 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TotalPhase 1Phase 2Phase 3Phase 4Phase 5Phase 6
11.1200370.0315460.0685270.8609210.0215220.1308710.006338
27.6141670.0121160.2321946.3965380.0540130.9158550.003059
328.3116350.0134950.75367823.5260220.2314563.7735700.013043
486.0601990.0283712.06853568.5053930.63359814.7692880.054599
5235.2719510.0261934.775386171.4608121.72387357.1282230.157032
6598.3469390.02067310.029798359.7604665.979951222.1676390.387727
\n", "" ], "text/plain": [ " Total Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6\n", "1 1.120037 0.031546 0.068527 0.860921 0.021522 0.130871 0.006338\n", "2 7.614167 0.012116 0.232194 6.396538 0.054013 0.915855 0.003059\n", "3 28.311635 0.013495 0.753678 23.526022 0.231456 3.773570 0.013043\n", "4 86.060199 0.028371 2.068535 68.505393 0.633598 14.769288 0.054599\n", "5 235.271951 0.026193 4.775386 171.460812 1.723873 57.128223 0.157032\n", "6 598.346939 0.020673 10.029798 359.760466 5.979951 222.167639 0.387727" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# produce a table of the time taken for various phases\n", "# the phases are:\n", "# 1: creation of campaign\n", "# 2: creation of samples\n", "# 3: running the cases\n", "# 4: calculation of statistics including Sobols\n", "# 5: returning of analysed results\n", "# 6: saving campaign and pickled results\n", "\n", "if __name__ == '__main__':\n", "\n", " Timings = pd.DataFrame(np.array([R[r]['times'] for r in list(R.keys())]), \n", " columns=['Total', 'Phase 1', 'Phase 2', 'Phase 3', 'Phase 4', 'Phase 5', 'Phase 6'], \n", " index=[R[r]['order'] for r in list(R.keys())])\n", " Timings.to_csv(open('Timings.csv', 'w'))\n", " display(Timings)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:46:33.025560Z", "start_time": "2021-06-07T14:46:32.336090Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the convergence of the mean and standard deviation to that of the highest order\n", "\n", "if __name__ == '__main__':\n", " last = -1\n", " O = [R[r]['order'] for r in list(R.keys())]\n", " if len(O[0:last]) > 0:\n", " plt.figure()\n", " plt.semilogy([o for o in O[0:last]],\n", " [np.sqrt(np.mean((R[o]['results'].describe('te', 'mean') - \n", " R[O[last]]['results'].describe('te', 'mean'))**2)) for o in O[0:last]],\n", " 'o-', label='mean')\n", " plt.semilogy([o for o in O[0:last]],\n", " [np.sqrt(np.mean((R[o]['results'].describe('te', 'std') - \n", " R[O[last]]['results'].describe('te', 'std'))**2)) for o in O[0:last]],\n", " 'o-', label='std')\n", " plt.xlabel('SC order')\n", " plt.ylabel('RMSerror compared to order=%s' % (O[last]))\n", " plt.legend(loc=0)\n", " plt.savefig('Convergence_mean_std.png')\n", " plt.savefig('Convergence_mean_std.pdf')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:46:33.521428Z", "start_time": "2021-06-07T14:46:33.027332Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the convergence of the first sobol to that of the highest order\n", "\n", "if __name__ == '__main__':\n", " last = -1\n", " O = [R[r]['order'] for r in list(R.keys())]\n", " if len(O[0:last]) > 0:\n", " plt.figure()\n", " O = [R[r]['order'] for r in list(R.keys())]\n", " for v in list(R[O[last]]['results'].sobols_first('te').keys()):\n", " plt.semilogy([o for o in O[0:last]],\n", " [np.sqrt(np.mean((R[o]['results'].sobols_first('te')[v] - \n", " R[O[last]]['results'].sobols_first('te')[v])**2)) for o in O[0:last]],\n", " 'o-',\n", " label=v)\n", " plt.xlabel('SC order')\n", " plt.ylabel('RMSerror for 1st sobol compared to order=%s' % (O[last]))\n", " plt.legend(loc=0)\n", " plt.savefig('Convergence_sobol_first.png')\n", " plt.savefig('Convergence_sobol_first.pdf')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:46:34.299946Z", "start_time": "2021-06-07T14:46:33.522285Z" }, "code_folding": [ 0 ], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Problem with some of the percentiles\n", "Problem with some of the percentiles\n", "Problem with sobols_second\n", "Problem with sobols_total\n", "Problem with distribution\n", "Problem with sobols_second\n", "Problem with sobols_total\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot a standard set of graphs for the highest order case\n", "\n", "if __name__ == '__main__':\n", " last = -1\n", " title = 'fusion test case with SC order = %i' % list(R.values())[last]['order']\n", " plot_Te(list(R.values())[last]['results'], title=title,)\n", " plot_ne(list(R.values())[last]['results'], title=title)\n", " plot_sobols_first(list(R.values())[last]['results'], title=title)\n", " try:\n", " plot_sobols_second(list(R.values())[last]['results'], title=title)\n", " except:\n", " print('Problem with sobols_second')\n", " try:\n", " plot_sobols_total(list(R.values())[last]['results'], title=title)\n", " except:\n", " print('Problem with sobols_total')\n", " try:\n", " plot_distribution(list(R.values())[last]['results'], list(R.values())[last]['results_df'], title=title)\n", " except:\n", " print('Problem with distribution')\n", " plot_sobols_first(list(R.values())[last]['results'], title=title, field='ne')\n", " try:\n", " plot_sobols_second(list(R.values())[last]['results'], title=title, field='ne')\n", " except:\n", " print('Problem with sobols_second')\n", " try:\n", " plot_sobols_total(list(R.values())[last]['results'], title=title, field='ne')\n", " except:\n", " print('Problem with sobols_total')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:46:57.859034Z", "start_time": "2021-06-07T14:46:34.301126Z" }, "code_folding": [ 0 ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:21<00:00, 46.05it/s]\n" ] } ], "source": [ "# prepare the test data\n", "\n", "if __name__ == '__main__':\n", "\n", " test_campaign = uq.Campaign(name='fusion_pce.') \n", "\n", " # Add the app (automatically set as current app)\n", " test_campaign.add_app(name=\"fusion\", params=define_params(), \n", " actions=uq.actions.Actions(uq.actions.ExecutePython(run_fusion_model)))\n", "\n", " # Associate a sampler with the campaign\n", " test_campaign.set_sampler(uq.sampling.quasirandom.LHCSampler(vary=define_vary(), count=100))\n", "\n", " # Perform the actions\n", " test_campaign.execute(nsamples=1000).collate(progress_bar=True)\n", "\n", " # Collate the results\n", " test_df = test_campaign.get_collation_result()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:47:38.303027Z", "start_time": "2021-06-07T14:46:57.859910Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "# calculate the SC surrogates\n", "if __name__ == '__main__':\n", "\n", " test_points = test_df[test_campaign.get_active_sampler().vary.get_keys()]\n", " test_results = test_df['te'].values\n", " test_predictions = {}\n", " for i in list(R.keys()):\n", " test_predictions[i] = np.squeeze(np.array(R[i]['results'].surrogate()(test_points)['te']))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:47:38.941586Z", "start_time": "2021-06-07T14:47:38.304875Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the performance of the SC surrogates\n", "if __name__ == '__main__':\n", "\n", " for i in list(R.keys()):\n", " plt.semilogy(R[i]['results'].describe('rho', 'mean'), \n", " np.sqrt(((test_predictions[i] - test_results)**2).mean(axis=0)) / test_results.mean(axis=0), \n", " label='SC order %s with %s samples' % (R[i]['order'], R[i]['number_of_samples']))\n", " plt.xlabel('rho [m]') ; plt.ylabel('fractional RMS for predicted Te') ; plt.legend(loc=0)\n", " plt.title('Performance of the SC surrogate')\n", " plt.savefig('SC_surrogate.png')\n", " plt.savefig('SC_surrogate.pdf')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2021-06-07T14:47:39.183173Z", "start_time": "2021-06-07T14:47:38.942333Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the convergence of the surrogate based on 1000 random points\n", "if __name__ == '__main__':\n", " _o = []\n", " _RMS = []\n", " for r in R.values():\n", " _RMS.append((np.sqrt((((test_predictions[r['order']] - test_results) / test_results)**2).mean())))\n", " _o.append(r['order'])\n", "\n", " plt.figure()\n", " plt.semilogy(_o, _RMS, 'o-')\n", " plt.xlabel('SC order')\n", " plt.ylabel('fractional RMS error for the SC surrogate')\n", " plt.legend(loc=0)\n", " plt.savefig('Convergence_SC_surrogate.png')\n", " plt.savefig('Convergence_SC_surrogate.pdf')" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "executable": " /usr/bin/env python", "main_language": "python", "notebook_metadata_filter": "-all" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.4" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 4 }